The joys of having to discuss another statistical question: when I was thinking about this question I was like what data? Who was more likely to win the grand final St Helens or Leeds Rhinos? Which mascara would make my eyes lashes longer or which aftershave is more likely to help the boys get laid? We receive data every minute of every day with any action I take. However in these situations I don’t actually have time to make a statistical analysis on what best course of action to take except when I’m in the bookies. So in everyday life we don’t really need stats to understand what is best to do and somehow we seem to cope and my eyelashes still look good.
However, when we spend hours doing our research and collecting our data, stats are a good tool to use when trying to understand our data and can be used to represent our findings in a single line of a statistical statement and with the help of the beautiful invention of SPSS which does this all for us we can easily analyze our data. With the help of graphs and tables we can discuss our findings. If one relies purely on stats, they may miss something crucial and glaringly obvious about the data being analyzed. An experimenter can analyze the data in the wrong way or choose accidentally not to take into account something that has controlled the course of the data because they may not believe its important data with data such as age or gender.
We do stats to help us to understand our data in a manageable concept however we do need to take into account other aspects of our research to gain a representative conclusion of our study.